yake
Single-document unsupervised keyword extraction (by LIAAD)
flashtext
Extract Keywords from sentence or Replace keywords in sentences. (by vi3k6i5)
| yake | flashtext | |
|---|---|---|
| 5 | 8 | |
| 1,656 | 5,598 | |
| 0.8% | - | |
| 3.0 | 0.0 | |
| 11 months ago | 5 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
yake
Posts with mentions or reviews of yake.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-14.
- Show HN: Whisper.cpp and YAKE to Analyse Voice Reflections [iOS]
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Simplest keyword extractor
Personally I prefer using YAKE.
- What method should be used to tag specific texts, when the dataset is too small for training a model?
- Is there any YAKE (yet another keyword extractor) implementation in R? Unsupervised Approach for Automatic Keyword Extraction using Text Statistical Features.
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Alternate approaches to TF-IDF?
You can look for usage here: https://github.com/LIAAD/yake and there is also a reference section with publications for more details of how this works. From what I remember, each keyphrase candidate is assigned an aggregated score based on various features: position in the text, casing, frequency, surrounding text frequency...
flashtext
Posts with mentions or reviews of flashtext.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-08-14.
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Show HN: LLMs can generate valid JSON 100% of the time
I have some other comment on this thread where I point out why I don’t think it’s superficial. Would love to get your feedback on that if you feel like spending more time on this thread.
But it’s not obscure? FlashText was a somewhat popular paper at the time (2017) with a popular repo (https://github.com/vi3k6i5/flashtext). Their paper was pretty derivative of Aho-Corasick, which they cited. If you think they genuinely fucked up, leave an issue on their repo (I’m, maybe to your surprise lol, not the author).
Anyway, I’m not a fan of the whatabboutery here. I don’t think OG’s paper is up to snuff on its lit review - do you?
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[P] what is the most efficient way to pattern matching word-to-word?
The library flashtext basically creates these tries based on keywords you give it.
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What is the most efficient way to find substrings in strings?
Seems like https://github.com/vi3k6i5/flashtext would be better suited here.
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[P] Library for end-to-end neural search pipelines
I started developing this tool after using haystack. Pipelines are easier to build with cherche because of the operators. Also, cherche offers FlashText, Lunr.py retrievers that are not available in Haystack and that I needed for the project I wanted to solve. Haystack is clearly more complete but I think also more complex to use.
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How can I speed up thousands of re.subs()?
For the text part not requiring regex, https://github.com/vi3k6i5/flashtext might help
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My first NLP pipeline using SpaCy: detect news headlines with company acquisitions
Spacy for parsing the Headlines, remove stop words etc. might be ok but I think the problem is quite narrow so a set of fixed regex searches might work quite well. If regex is too slow, try: https://github.com/vi3k6i5/flashtext
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What tech do I need to learn to programmatically parse ingredients from a recipe?
I would probably use something like [flashtext](https://github.com/vi3k6i5/flashtext) which should not be too hard to port to kotlin.
- Quickest way to check that 14000 strings arent in An original string.
What are some alternatives?
When comparing yake and flashtext you can also consider the following projects:
rake-nltk - Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
KeyBERT - Minimal keyword extraction with BERT